Instructions to use SinclairSchneider/dbrx-instruct-quantization-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SinclairSchneider/dbrx-instruct-quantization-fixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SinclairSchneider/dbrx-instruct-quantization-fixed

SGLang

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SinclairSchneider/dbrx-instruct-quantization-fixed" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SinclairSchneider/dbrx-instruct-quantization-fixed" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SinclairSchneider/dbrx-instruct-quantization-fixed with Docker Model Runner:
```
docker model run hf.co/SinclairSchneider/dbrx-instruct-quantization-fixed
```

SinclairSchneider commited on Apr 2, 2024

Commit

dfa4056

verified ·

1 Parent(s): 52aabf1

Update modeling_dbrx.py

Browse files

gradient_checkpointing fixed

Files changed (1) hide show

modeling_dbrx.py +7 -7

modeling_dbrx.py CHANGED Viewed

@@ -1093,13 +1093,13 @@ class DbrxModel(DbrxPreTrainedModel):
                 block_outputs = self._gradient_checkpointing_func(
                     block.__call__,
                     hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_values=past_key_values,
-                    output_attentions=output_attentions,
-                    output_router_logits=output_router_logits,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
                 )
             else:
                 block_outputs = block(

                 block_outputs = self._gradient_checkpointing_func(
                     block.__call__,
                     hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                    cache_position,
                 )
             else:
                 block_outputs = block(